Digitally Reproducing MOF Material Synthesis Conditions For Quantitative Comparison

Metal organic framework (MOF) materials represent an exciting new development in porous materials science, with myriad applications in fields as diverse as carbon capture, catalysis, and water harvesting. However, with tens of thousands of MOF syntheses identified, reproducing a reported framework or identifying the best synthesis route is a complex and often frustrating task. Further, these syntheses are often reported as plain text, preventing quantitative comparison between different studies. In this research theme, we are applying text mining and natural language processing tools to digitise MOF synthesis methods. Using this depth-first text mining framework, we aim to perform optimisation of synthesis methods solely from pre-existing published data, accelerating methodology development and hence implementation of MOF materials in larger-scale applications.